# Lightweight and Scalable Particle Tracking and Motion Clustering of 3D Cell Trajectories

## TL;DR

This paper introduces a fast, scalable, and unsupervised computational framework for detecting, tracking, and clustering 3D cell trajectories, specifically applied to studying Toxoplasma gondii motility in microscopy videos.

## Contribution

The authors develop a lightweight, scalable pipeline combining multiple modules for 3D cell tracking and motion clustering, optimized with task distribution and parallelism.

## Key findings

- High accuracy in cell detection and tracking
- Significant speedup through parallel processing
- Effective clustering of T. gondii motion phenotypes

## Abstract

Tracking cell particles in 3D microscopy videos is a challenging task but is of great significance for modeling the motion of cells. Proper characterization of the cell's shape, evolution, and their movement over time is crucial to understanding and modeling the mechanobiology of cell migration in many diseases. One in particular, toxoplasmosis is the disease caused by the parasite Toxoplasma gondii. Roughly, one-third of the world's population tests positive for T. gondii. Its virulence is linked to its lytic cycle, predicated on its motility and ability to enter and exit nucleated cells; therefore, studies elucidating its motility patterns are critical to the eventual development of therapeutic strategies. Here, we present a computational framework for fast and scalable detection, tracking, and identification of T. gondii motion phenotypes in 3D videos, in a completely unsupervised fashion. Our pipeline consists of several different modules including preprocessing, sparsification, cell detection, cell tracking, trajectories extraction, parametrization of the trajectories; and finally, a clustering step. Additionally, we identified the computational bottlenecks, and developed a lightweight and highly scalable pipeline through a combination of task distribution and parallelism. Our results prove both the accuracy and performance of our method.